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Lorenzo Iovino 2019-09-11 16:04:45 +02:00
parent ae477fc3df
commit 0109aab0f5
15 changed files with 918 additions and 586 deletions
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0
DataGeneratorPuppets/package-lock.json → DataGenerator/package-lock.json generated
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0
DataGeneratorPuppets/package.json → DataGenerator/package.json
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0
DataGeneratorPuppets/src/Puppet.ts → DataGenerator/src/Puppet.ts
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0
DataGeneratorPuppets/src/event-flow/MouseEventFlow.ts → DataGenerator/src/event-flow/MouseEventFlow.ts
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13
DataGeneratorPuppets/src/main.ts → DataGenerator/src/main.ts
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@ -3,12 +3,6 @@ import {flows} from "../../DataGeneratorPuppets/src/datas/flow";
function main() { function main() {
/*for(let i = 1; i <= 5; i++) {
let flowIndex = Math.floor(Math.random() * flows.length + 0);
const puppet = new Puppet('', flows[flowIndex].name, 'bot_'+i, flows[flowIndex].data);
puppet.run();
}*/
let puppetCounter = 0; let puppetCounter = 0;
let loop = true; let loop = true;
let counter = 0; let counter = 0;
@ -30,13 +24,6 @@ function main() {
}); });
} }
}, 50) }, 50)
/*
const puppet2 = new Puppet('', flows[flowIndex].name, 'bot_'+puppetCounter++, flows[flowIndex].data).run();
const puppet3 = new Puppet('', flows[flowIndex].name, 'bot_'+puppetCounter++, flows[flowIndex].data).run();
const puppet4 = new Puppet('', flows[flowIndex].name, 'bot_'+puppetCounter++, flows[flowIndex].data).run();
const puppet5 = new Puppet('', flows[flowIndex].name, 'bot_'+puppetCounter++, flows[flowIndex].data).run('infinite');*/
} }
main(); main();

0
DataGeneratorPuppets/tsconfig.json → DataGenerator/tsconfig.json
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60
Predictor/PredictorNeuralNetwork/Predictor.py
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@ -8,15 +8,38 @@ import matplotlib.pyplot as plt
import matplotlib.image as mpimg import matplotlib.image as mpimg
import cv2 import cv2
import numpy as np import numpy as np
import os
import shutil
import random
img_width, img_height = 150, 150 img_width, img_height = 150, 150
train_data_dir = './UserflowPredictorSystem/predictor/datas/train' train_data_dir = './Predictor/PredictorNeuralNetwork/datas/userflows/train/'
validation_data_dir = './UserflowPredictorSystem/predictor/datas/test' validation_data_dir = './Predictor/PredictorNeuralNetwork/datas/userflows/test/'
nb_train_samples = 2000 nb_train_samples = 2000
nb_validation_samples = 800 nb_validation_samples = 800
epochs = 100 epochs = 20
batch_size = 16 batch_size = 16
def copytree(src, dst, symlinks=False, ignore=None):
for item in os.listdir(src):
s = os.path.join(src, item)
d = os.path.join(dst, item)
if os.path.isdir(s):
shutil.copytree(s, d, symlinks, ignore)
else:
shutil.copy2(s, d)
def populateDatas():
copytree('./Predictor/PredictorWebService/trainingImages/virtual', './Predictor/PredictorNeuralNetwork/datas/userflows/train')
offset = 0
for folder in os.listdir('./Predictor/PredictorNeuralNetwork/datas/userflows/train'):
numberOfFiles = len([name for name in os.listdir('./Predictor/PredictorNeuralNetwork/datas/userflows/train/' + folder)])
numberOfTestFiles = int(numberOfFiles / 10)
for idx in range(0,numberOfTestFiles):
file = os.listdir('./Predictor/PredictorNeuralNetwork/datas/userflows/train/' + folder)[random.randint(0,numberOfTestFiles)]
shutil.copy2("./Predictor/PredictorNeuralNetwork/datas/userflows/train/" + folder + "/" + file, './Predictor/PredictorNeuralNetwork/datas/userflows/test/' + str(idx + offset) + '.png')
shutil.copy2("./Predictor/PredictorNeuralNetwork/datas/userflows/train/" + folder + "/" + file, './Predictor/PredictorNeuralNetwork/datas/userflows/test/' + str(idx + offset) + '_SEPARATOR.png')
offset = numberOfTestFiles
if K.image_data_format() == 'channels_first': if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height) input_shape = (3, img_width, img_height)
@ -45,7 +68,7 @@ def create_model():
model.add(Activation('sigmoid')) model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy', model.compile(loss='categorical_crossentropy',
optimizer='rmsprop', optimizer='rmsprop',
metrics=['accuracy']) metrics=['accuracy'])
return model return model
@ -66,13 +89,13 @@ def train_model(model):
train_data_dir, train_data_dir,
target_size=(img_width, img_height), target_size=(img_width, img_height),
batch_size=batch_size, batch_size=batch_size,
class_mode='binary') class_mode='categorical')
validation_generator = test_datagen.flow_from_directory( validation_generator = test_datagen.flow_from_directory(
validation_data_dir, validation_data_dir,
target_size=(img_width, img_height), target_size=(img_width, img_height),
batch_size=batch_size, batch_size=batch_size,
class_mode='binary') class_mode='categorical')
model.fit_generator( model.fit_generator(
train_generator, train_generator,
@ -81,7 +104,7 @@ def train_model(model):
validation_data=validation_generator, validation_data=validation_generator,
validation_steps=nb_validation_samples // batch_size) validation_steps=nb_validation_samples // batch_size)
model.save_weights('./UserflowPredictorSystem/first_try2.h5') model.save_weights('./Predictor/PredictorNeuralNetwork/weights/userflows.h5')
return model return model
@ -93,29 +116,26 @@ def load_trained_model(weights_path):
def predict(number, model): def predict(number, model):
img = cv2.imread("./UserflowPredictorSystem/predictor/datas/test/" + str(number) + ".jpg") img = cv2.imread("./Predictor/PredictorNeuralNetwork/datas/userflows/test/" + str(number) + ".png")
im = mpimg.imread("./UserflowPredictorSystem/predictor/datas/test/" + str(number) + ".jpg") im = mpimg.imread("./Predictor/PredictorNeuralNetwork/datas/userflows/test/" + str(number) + ".png")
plt.imshow(im) plt.imshow(im)
img = cv2.resize(img, (img_width,img_height)) img = cv2.resize(img, (img_width,img_height))
img = img.reshape(1, img_width, img_height, 3) img = img.reshape(1, img_width, img_height, 3)
res = model.predict(img) res = model.predict(img)
if res == 1: print(res)
print('DOG')
else:
print('CAT')
model = create_model() model = create_model()
populateDatas()
model = train_model(model) model = train_model(model)
import os
os.getcwd() os.getcwd()
trained_model = load_trained_model("./UserflowPredictorSystem/first_try2.h5") trained_model = load_trained_model("./Predictor/PredictorNeuralNetwork/weights/userflows.h5")
trained_model.summary() trained_model.summary()
import random num = random.randint(1,10)
predict(random.randint(1,12500), trained_model) print(num)
predict('lolly', model) predict(num, trained_model)
print(np.argmax(trained_model.predict(img)))
print(np.argmax(loaded_model.predict(img)))

119
Predictor/PredictorNeuralNetwork/PredictorDEMO_CatVsDog.py Normal file
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@ -0,0 +1,119 @@
from keras.models import Sequential
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.image import ImageDataGenerator
from keras.layers import Conv2D, MaxPooling2D
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import numpy as np
img_width, img_height = 150, 150
train_data_dir = './Predictor/PredictorNeuralNetwork/datas/catVsDog/train/'
validation_data_dir = './Predictor/PredictorNeuralNetwork/datas/catVsDog/test/'
nb_train_samples = 2000
nb_validation_samples = 800
epochs = 100
batch_size = 16
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
else:
input_shape = (img_width, img_height, 3)
def create_model():
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
return model
def train_model(model):
# this is the augmentation configuration we will use for training
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
# this is the augmentation configuration we will use for testing:
# only rescaling
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples // batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=nb_validation_samples // batch_size)
model.save_weights('./Predictor/PredictorNeuralNetwork/weights/catVsDog2.h5')
return model
def load_trained_model(weights_path):
model = create_model()
model.load_weights(weights_path)
return model
def predict(number, model):
img = cv2.imread("./Predictor/PredictorNeuralNetwork/datas/catVsDog/test/" + str(number) + ".jpg")
im = mpimg.imread("./Predictor/PredictorNeuralNetwork/datas/catVsDog/test/" + str(number) + ".jpg")
plt.imshow(im)
img = cv2.resize(img, (img_width,img_height))
img = img.reshape(1, img_width, img_height, 3)
res = model.predict(img)
if res == 1:
print('DOG')
else:
print('CAT')
model = create_model()
model = train_model(model)
import os
os.getcwd()
trained_model = load_trained_model("./Predictor/PredictorNeuralNetwork/weights/catVsDog.h5")
trained_model.summary()
import random
predict(random.randint(1,12500), trained_model)
print(np.argmax(trained_model.predict(img)))

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Predictor/PredictorNeuralNetwork/weights/first_try.h5
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78
Predictor/PredictorWebService/build/config.gypi Normal file
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@ -0,0 +1,78 @@
# Do not edit. File was generated by node-gyp's "configure" step
{
"target_defaults": {
"cflags": [],
"default_configuration": "Release",
"defines": [],
"include_dirs": [],
"libraries": [],
"msbuild_toolset": "v141",
"msvs_windows_target_platform_version": "10.0.17763.0"
},
"variables": {
"asan": 0,
"build_v8_with_gn": "false",
"coverage": "false",
"debug_nghttp2": "false",
"enable_lto": "false",
"enable_pgo_generate": "false",
"enable_pgo_use": "false",
"force_dynamic_crt": 0,
"host_arch": "x64",
"icu_data_in": "..\\..\\deps/icu-small\\source/data/in\\icudt64l.dat",
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929
Predictor/PredictorWebService/package-lock.json generated
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File diff suppressed because it is too large Load diff

2
Predictor/PredictorWebService/src/image-creator/ImageCreatorService.ts
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@ -39,7 +39,7 @@ export class ImageCreatorService {
}); });
//todo da cambiare le dimensioni e renderle dinamiche //todo da cambiare le dimensioni e renderle dinamiche
const canvas = createCanvas(1000, 800); const canvas = createCanvas(150, 150);
const ctx = canvas.getContext('2d'); const ctx = canvas.getContext('2d');
this.printHTMLElements(htmlElements, ctx); this.printHTMLElements(htmlElements, ctx);
this.printMouseClick(mouseClicks, ctx); this.printMouseClick(mouseClicks, ctx);